{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "voluntary-signal",
   "metadata": {},
   "source": [
    "## Description:\n",
    "这里是DSIN的一个demo， 主要分为数据读取与处理，模型搭建，模型的训练三大模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "metric-prediction",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:23.688016Z",
     "start_time": "2021-03-12T07:43:19.928070Z"
    }
   },
   "outputs": [],
   "source": [
    "# python基础包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 特征处理与数据集划分\n",
    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from utils import DenseFeat, SparseFeat, VarLenSparseFeat\n",
    "\n",
    "# 导入模型\n",
    "from DSIN import DSIN\n",
    "\n",
    "# 模型训练相关\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "from tensorflow.keras.metrics import AUC\n",
    "from tensorflow.keras.losses import binary_crossentropy\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "# 一些相关设置\n",
    "import warnings\n",
    "plt.style.use('fivethirtyeight')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "better-vector",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "hundred-rapid",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:25.120944Z",
     "start_time": "2021-03-12T07:43:25.084043Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"读取数据\"\"\"\n",
    "samples_data = pd.read_csv(\"data/movie_sample.txt\", sep=\"\\t\", header = None)\n",
    "samples_data.columns = [\"user_id\", \"gender\", \"age\", \"hist_movie_id\", \"hist_len\", \"movie_id\", \"movie_type_id\", \"label\"]\n",
    "\n",
    "#  把历史行为序列转成整数列表\n",
    "def str2list(x):\n",
    "    return [int(i) for i in x.split(',')]\n",
    "samples_data['hist_movie_id'] = samples_data['hist_movie_id'].apply(lambda x: str2list(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "intense-uruguay",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:25.811026Z",
     "start_time": "2021-03-12T07:43:25.781107Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"会话处理\"\"\"\n",
    "# 把hist_movie_id拆成6个会话， 每个会话的长度是10， 会话不够的0填充\n",
    "for i in range(5):\n",
    "    samples_data['sess' + str(i+1)] = samples_data['hist_movie_id'].apply(lambda x: x[i*10:(i+1)*10])\n",
    "\n",
    "# 每个样本的会话个数  这里的hist_len其实就是sess_nums了，这里这么写是想练一波python处理\n",
    "sess_nums = np.array([len([int(i) for i in l if int(i) != 0]) // 10 + 1 for l in samples_data['hist_movie_id']])\n",
    "sess_nums = np.array([i if i <= 5 else 5 for i in sess_nums])   # 这个是正好50个行为的这种，按照上面那个算会是6，但其实5个会话\n",
    "sess_max_count = 5\n",
    "\n",
    "del samples_data['hist_movie_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "worldwide-belief",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:27.352190Z",
     "start_time": "2021-03-12T07:43:27.327256Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hist_len</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>movie_type_id</th>\n",
       "      <th>label</th>\n",
       "      <th>sess1</th>\n",
       "      <th>sess2</th>\n",
       "      <th>sess3</th>\n",
       "      <th>sess4</th>\n",
       "      <th>sess5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>[186, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1378</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>141</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>[5, 207, 198, 147, 109, 82, 80, 57, 84, 200]</td>\n",
       "      <td>[88, 174, 193, 95, 183, 64, 162, 37, 28, 156]</td>\n",
       "      <td>[56, 135, 152, 38, 41, 130, 108, 23, 81, 105]</td>\n",
       "      <td>[137, 71, 86, 69, 68, 185, 169, 67, 11, 31]</td>\n",
       "      <td>[93, 92, 202, 83, 97, 126, 128, 197, 168, 32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>[5, 207, 198, 147, 109, 82, 80, 57, 84, 200]</td>\n",
       "      <td>[88, 174, 193, 95, 183, 64, 162, 37, 28, 156]</td>\n",
       "      <td>[56, 135, 152, 38, 41, 130, 108, 23, 81, 105]</td>\n",
       "      <td>[137, 71, 86, 69, 68, 185, 169, 67, 11, 31]</td>\n",
       "      <td>[93, 92, 202, 83, 97, 126, 128, 197, 168, 32]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  gender  age  hist_len  movie_id  movie_type_id  label  \\\n",
       "0           1       1    1         1       112              2      1   \n",
       "1           1       1    1         1        38              5      0   \n",
       "1378        3       2    3        50       141              9      0   \n",
       "1379        3       2    3        50        15              3      0   \n",
       "\n",
       "                                             sess1  \\\n",
       "0                 [186, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "1                 [186, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "1378  [5, 207, 198, 147, 109, 82, 80, 57, 84, 200]   \n",
       "1379  [5, 207, 198, 147, 109, 82, 80, 57, 84, 200]   \n",
       "\n",
       "                                              sess2  \\\n",
       "0                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "1                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "1378  [88, 174, 193, 95, 183, 64, 162, 37, 28, 156]   \n",
       "1379  [88, 174, 193, 95, 183, 64, 162, 37, 28, 156]   \n",
       "\n",
       "                                              sess3  \\\n",
       "0                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "1                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "1378  [56, 135, 152, 38, 41, 130, 108, 23, 81, 105]   \n",
       "1379  [56, 135, 152, 38, 41, 130, 108, 23, 81, 105]   \n",
       "\n",
       "                                            sess4  \\\n",
       "0                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "1                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]   \n",
       "1378  [137, 71, 86, 69, 68, 185, 169, 67, 11, 31]   \n",
       "1379  [137, 71, 86, 69, 68, 185, 169, 67, 11, 31]   \n",
       "\n",
       "                                              sess5  \n",
       "0                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  \n",
       "1                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  \n",
       "1378  [93, 92, 202, 83, 97, 126, 128, 197, 168, 32]  \n",
       "1379  [93, 92, 202, 83, 97, 126, 128, 197, 168, 32]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples_data.head(2).append(samples_data.tail(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "trained-gibson",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:28.434730Z",
     "start_time": "2021-03-12T07:43:28.425712Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"数据集\"\"\"\n",
    "X = samples_data.drop(columns='label')\n",
    "y = samples_data[\"label\"]\n",
    "\n",
    "# 构建mask 是个列表，每个元素代表每个session里面序列的mask\n",
    "mask = []\n",
    "for i in range(sess_max_count):\n",
    "    mask.append(\n",
    "        np.array([len([k for k in l if k != 0]) for l in X['sess'+str(i+1)]])\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "forward-oriental",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:29.508223Z",
     "start_time": "2021-03-12T07:43:29.479301Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"构建DSIN模型的输入格式\"\"\"\n",
    "X_train = {\"user_id\": np.array(X[\"user_id\"]), \\\n",
    "        \"gender\": np.array(X[\"gender\"]), \\\n",
    "        \"age\": np.array(X[\"age\"]), \\\n",
    "        \"movie_id\": np.array(X[\"movie_id\"]), \\\n",
    "        \"movie_type_id\": np.array(X[\"movie_type_id\"]), \\\n",
    "        \"hist_len\": np.array(X[\"hist_len\"]), \\\n",
    "        \"sess1\": np.stack(X['sess1']), \\\n",
    "        \"sess2\": np.stack(X['sess2']), \\\n",
    "        \"sess3\": np.stack(X['sess3']), \\\n",
    "        \"sess4\": np.stack(X['sess4']), \\\n",
    "        \"sess5\": np.stack(X['sess5']), \\\n",
    "        \"seq_length1\": mask[0], \\\n",
    "        \"seq_length2\": mask[1], \\\n",
    "        \"seq_length3\": mask[2], \\\n",
    "        \"seq_length4\": mask[3], \\\n",
    "        \"seq_length5\": mask[4], \\\n",
    "        \"sess_length\": sess_nums\n",
    "        }\n",
    "\n",
    "y_train = np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "touched-headquarters",
   "metadata": {},
   "source": [
    "## 模型建立"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "accompanied-camera",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:31.331427Z",
     "start_time": "2021-03-12T07:43:31.321453Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"特征封装\"\"\"\n",
    "\n",
    "feature_columns = [SparseFeat('user_id', max(samples_data[\"user_id\"])+1, embedding_dim=8), \n",
    "                    SparseFeat('gender', max(samples_data[\"gender\"])+1, embedding_dim=8), \n",
    "                    SparseFeat('age', max(samples_data[\"age\"])+1, embedding_dim=8), \n",
    "                    SparseFeat('movie_id', max(samples_data[\"movie_id\"])+1, embedding_dim=8),\n",
    "                    SparseFeat('movie_type_id', max(samples_data[\"movie_type_id\"])+1, embedding_dim=8),\n",
    "                    DenseFeat('hist_len', 1)]\n",
    "\n",
    "feature_columns += [VarLenSparseFeat('sess1', vocabulary_size=max(samples_data[\"movie_id\"])+1, embedding_dim=8, maxlen=10, length_name='seq_length1'),\n",
    "                    VarLenSparseFeat('sess2', vocabulary_size=max(samples_data[\"movie_id\"])+1, embedding_dim=8, maxlen=10, length_name='seq_length2'), \n",
    "                    VarLenSparseFeat('sess3', vocabulary_size=max(samples_data[\"movie_id\"])+1, embedding_dim=8, maxlen=10, length_name='seq_length3'), \n",
    "                    VarLenSparseFeat('sess4', vocabulary_size=max(samples_data[\"movie_id\"])+1, embedding_dim=8, maxlen=10, length_name='seq_length4'), \n",
    "                    VarLenSparseFeat('sess5', vocabulary_size=max(samples_data[\"movie_id\"])+1, embedding_dim=8, maxlen=10, length_name='seq_length5'), \n",
    "                   ]\n",
    "feature_columns += ['sess_length']\n",
    "\n",
    "# 行为特征列表，表示的是基础特征\n",
    "sess_feature_list = ['movie_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "stylish-savannah",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:34.614481Z",
     "start_time": "2021-03-12T07:43:34.607458Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"设置超参数\"\"\"\n",
    "learning_rate = 0.001\n",
    "batch_size = 64\n",
    "epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "suited-application",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:39.840690Z",
     "start_time": "2021-03-12T07:43:36.055254Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From E:\\Jupyter Notebook\\GitHubRepositories\\AI-RecommenderSystem\\DSIN\\DSIN.py:620: The name tf.keras.backend.get_session is deprecated. Please use tf.compat.v1.keras.backend.get_session instead.\n",
      "\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "movie_id (InputLayer)           [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sess1 (InputLayer)              [(None, 10)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sess2 (InputLayer)              [(None, 10)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sess3 (InputLayer)              [(None, 10)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sess4 (InputLayer)              [(None, 10)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sess5 (InputLayer)              [(None, 10)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "emb_movie_id (Embedding)        multiple             1672        movie_id[0][0]                   \n",
      "                                                                 movie_id[0][0]                   \n",
      "                                                                 sess1[0][0]                      \n",
      "                                                                 sess2[0][0]                      \n",
      "                                                                 sess3[0][0]                      \n",
      "                                                                 sess4[0][0]                      \n",
      "                                                                 sess5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "bias_encoding (BiasEncoding)    [(None, 10, 8), (Non 23          emb_movie_id[2][0]               \n",
      "                                                                 emb_movie_id[3][0]               \n",
      "                                                                 emb_movie_id[4][0]               \n",
      "                                                                 emb_movie_id[5][0]               \n",
      "                                                                 emb_movie_id[6][0]               \n",
      "__________________________________________________________________________________________________\n",
      "seq_length1 (InputLayer)        [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "seq_length2 (InputLayer)        [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "seq_length3 (InputLayer)        [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "seq_length4 (InputLayer)        [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "seq_length5 (InputLayer)        [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "user_id (InputLayer)            [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "gender (InputLayer)             [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "age (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "movie_type_id (InputLayer)      [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "transformer (Transformer)       (None, 1, 8)         720         bias_encoding[0][0]              \n",
      "                                                                 bias_encoding[0][0]              \n",
      "                                                                 seq_length1[0][0]                \n",
      "                                                                 bias_encoding[0][1]              \n",
      "                                                                 bias_encoding[0][1]              \n",
      "                                                                 seq_length2[0][0]                \n",
      "                                                                 bias_encoding[0][2]              \n",
      "                                                                 bias_encoding[0][2]              \n",
      "                                                                 seq_length3[0][0]                \n",
      "                                                                 bias_encoding[0][3]              \n",
      "                                                                 bias_encoding[0][3]              \n",
      "                                                                 seq_length4[0][0]                \n",
      "                                                                 bias_encoding[0][4]              \n",
      "                                                                 bias_encoding[0][4]              \n",
      "                                                                 seq_length5[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "emb_user_id (Embedding)         (None, 1, 8)         32          user_id[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "emb_gender (Embedding)          (None, 1, 8)         24          gender[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "emb_age (Embedding)             (None, 1, 8)         32          age[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "emb_movie_type_id (Embedding)   (None, 1, 8)         80          movie_type_id[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 5, 8)         0           transformer[0][0]                \n",
      "                                                                 transformer[1][0]                \n",
      "                                                                 transformer[2][0]                \n",
      "                                                                 transformer[3][0]                \n",
      "                                                                 transformer[4][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 8)            0           emb_user_id[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 8)            0           emb_gender[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 8)            0           emb_age[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 8)            0           emb_movie_id[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_4 (Flatten)             (None, 8)            0           emb_movie_type_id[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "bi_lstm (BiLSTM)                (None, 5, 8)         2176        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "hist_len (InputLayer)           [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 40)           0           flatten[0][0]                    \n",
      "                                                                 flatten_1[0][0]                  \n",
      "                                                                 flatten_2[0][0]                  \n",
      "                                                                 flatten_3[0][0]                  \n",
      "                                                                 flatten_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "attention_pooling_layer (Attent (None, 8)            51905       emb_movie_id[1][0]               \n",
      "                                                                 concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "attention_pooling_layer_1 (Atte (None, 8)            51905       emb_movie_id[1][0]               \n",
      "                                                                 bi_lstm[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 57)           0           hist_len[0][0]                   \n",
      "                                                                 concatenate[0][0]                \n",
      "                                                                 attention_pooling_layer[0][0]    \n",
      "                                                                 attention_pooling_layer_1[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "dense_8 (Dense)                 (None, 200)          11800       concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_9 (Dense)                 (None, 80)           16160       dense_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "sess_length (InputLayer)        [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_10 (Dense)                (None, 1)            81          dense_9[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 136,610\n",
      "Trainable params: 136,610\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\"\"\"构建DSIN模型\"\"\"\n",
    "model = DSIN(feature_columns, sess_feature_list, bias_encoding=True)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "former-identifier",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:42.011298Z",
     "start_time": "2021-03-12T07:43:41.858339Z"
    },
    "cell_style": "split"
   },
   "outputs": [],
   "source": [
    "\"\"\"模型编译\"\"\"\n",
    "model.compile(loss=binary_crossentropy, optimizer=Adam(learning_rate=learning_rate), metrics=[AUC()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "identified-scope",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "impressive-renewal",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:52.508210Z",
     "start_time": "2021-03-12T07:43:42.914860Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1104 samples, validate on 276 samples\n",
      "Epoch 1/50\n",
      "1104/1104 [==============================] - 3s 3ms/sample - loss: 0.5793 - auc: 0.5298 - val_loss: 0.5012 - val_auc: 0.4966\n",
      "Epoch 2/50\n",
      "1104/1104 [==============================] - 0s 243us/sample - loss: 0.4816 - auc: 0.5657 - val_loss: 0.4991 - val_auc: 0.4875\n",
      "Epoch 3/50\n",
      "1104/1104 [==============================] - 0s 254us/sample - loss: 0.4747 - auc: 0.5062 - val_loss: 0.4872 - val_auc: 0.4826\n",
      "Epoch 4/50\n",
      "1104/1104 [==============================] - 0s 257us/sample - loss: 0.4707 - auc: 0.5387 - val_loss: 0.5390 - val_auc: 0.4289\n",
      "Epoch 5/50\n",
      "1104/1104 [==============================] - 0s 255us/sample - loss: 0.4776 - auc: 0.5074 - val_loss: 0.4829 - val_auc: 0.4310\n",
      "Epoch 6/50\n",
      "1104/1104 [==============================] - 0s 259us/sample - loss: 0.4904 - auc: 0.5152 - val_loss: 0.5025 - val_auc: 0.4239\n",
      "Epoch 7/50\n",
      "1104/1104 [==============================] - 0s 225us/sample - loss: 0.4929 - auc: 0.5451 - val_loss: 0.5532 - val_auc: 0.2559\n",
      "Epoch 8/50\n",
      "1104/1104 [==============================] - ETA: 0s - loss: 0.4531 - auc: 0.6087\n",
      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "1104/1104 [==============================] - 1s 473us/sample - loss: 0.4531 - auc: 0.6087 - val_loss: 0.5457 - val_auc: 0.2452\n",
      "Epoch 9/50\n",
      "1104/1104 [==============================] - 0s 273us/sample - loss: 0.4269 - auc: 0.6690 - val_loss: 0.5482 - val_auc: 0.2379\n",
      "Epoch 10/50\n",
      "1104/1104 [==============================] - 1s 600us/sample - loss: 0.4229 - auc: 0.6749 - val_loss: 0.5506 - val_auc: 0.2321\n"
     ]
    }
   ],
   "source": [
    "\"\"\"模型训练\"\"\"\n",
    "callbacks = [\n",
    "    EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),   # 早停\n",
    "    ReduceLROnPlateau(monitor='val_loss', patience=3, factor=0.01, verbose=1)  # 调整学习率\n",
    "]\n",
    "history = model.fit(X_train, \n",
    "                    y_train, \n",
    "                    epochs=epochs, \n",
    "                    validation_split=0.2, \n",
    "                    batch_size=batch_size,\n",
    "                    callbacks = callbacks\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "thirty-tennis",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:43:56.526972Z",
     "start_time": "2021-03-12T07:43:56.326508Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"可视化下看看训练情况\"\"\"\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alleged-pencil",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "## 模型架构可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "broad-attention",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:44:04.589649Z",
     "start_time": "2021-03-12T07:44:01.657132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "keras.utils.plot_model(model, to_file='./DSIN.png', show_shapes=True)"
   ]
  }
 ],
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  "kernelspec": {
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